Explore the vast range of titles available.

Legumes and actinorhizal plants are capable of forming root nodules symbiosis with rhizobia and Frankia bacteria. All these nodulating species belong to the nitrogen fixation clade. Most likely, nodulation evolved once in the last common ancestor of this clade. NIN (NODULE INCEPTION) is a transcription factor that is essential for nodulation in all studied species. Therefore, it seems probable that it was recruited at the start when nodulation evolved. NIN is the founding member of the NIN-like protein (NLP) family. It arose by duplication, and this occurred before nodulation evolved. Therefore, several plant species outside the nitrogen fixation clade have NLP(s), which is orthologous to NIN. In this review, we discuss how NIN has diverged from the ancestral NLP, what minimal changes would have been essential for it to become a key transcription controlling nodulation, and which adaptations might have evolved later.

Oxygen redox catalysis, including the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), is crucial in determining the electrochemical performance of energy conversion and storage devices such as fuel cells, metal–air batteries,and electrolyzers. The rational design of electrochemical catalysts replaces the traditional trial‐and‐error methods and thus promotes the R&D process. Identifying descriptors that link structure and activity as well as selectivity of catalysts is the key for rational design. In the past few decades, two types of descriptors including bulk‐ and surface‐based have been developed to probe the structure–property relationships. Correlating the current descriptors to one another will promote the understanding of the underlying physics and chemistry, triggering further development of more universal descriptors for the future design of electrocatalysts. Herein, the current benchmark activity descriptors for oxygen electrocatalysis as well as their applications are reviewed. Particular attention is paid to circumventing the scaling relationship of oxygen‐containing intermediates. For hybrid materials, multiple descriptors will show stronger predictive power by considering more factors such as interface reconstruction, confinement effect, multisite adsorption, etc. Machine learning and high‐throughput simulations can thus be crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. The descriptors for the activity of oxygen redox reactions are summarized with special attention being paid to their applications as well as the challenges and outlook of these descriptor‐based approaches.

Currently, many fault diagnosis methods for rolling bearings based on deep learning are facing two main challenges. Firstly, the deep learning model exhibits poor diagnostic performance and limited generalization ability in the presence of noise signals and varying loads. Secondly, there is incomplete utilization of fault information and inadequate extraction of fault features, leading to the low diagnostic accuracy of the model. To address these problems, this paper proposes an improved dual-branch convolutional capsule neural network for rolling bearing fault diagnosis. This method converts the collected bearing vibration signals into grayscale images to construct a grayscale image dataset. By fully considering the types of bearing faults and damage diameters, the data are labeled using a dual-label format. A multi-scale convolution module is introduced to extract features from the data and maximize feature information extraction. Additionally, a coordinate attention mechanism is incorporated into this module to better extract useful channel features and enhance feature extraction capability. Based on adaptive fusion between fault type (damage diameter) features and labels, a dual-branch convolutional capsule neural network model for rolling bearing fault diagnosis is established. The model was experimentally validated using both Case Western Reserve University’s bearing dataset and self-made datasets. The experimental results demonstrate that the fault type branch of the model achieves an accuracy rate of 99.88%, while the damage diameter branch attains an accuracy rate of 99.72%. Both branches exhibit excellent classification performance and display robustness against noise interference and variable working conditions. In comparison with other algorithm models cited in the reference literature, the diagnostic capability of the model proposed in this study surpasses them. Furthermore, the generalization ability of the model is validated using a self-constructed laboratory dataset, yielding an average accuracy rate of 94.25% for both branches.

This paper investigates the adaptive robust control problem based on reinforcement learning for an affine nonlinear system with unknown time‐varying uncertainty. Inspired by the ability to estimate uncertainty of neural network, a novel policy iteration algorithm is proposed which alternates between the value evaluation, uncertainty estimation, and policy update steps until the adaptive robust control law is obtained. Especially during the step of uncertainty estimation, the unknown time‐varying uncertainty is approximated by a radial basis function neural network and introduce it into the reinforcement learning framework. By designing an appropriate utility function, the algorithm improves both convergence rate and final approximate error comparing with existing reinforcement learning algorithm. The Lyapunov stability theorem provides theoretical demonstrations of the stability and convergence. Furthermore, the uniformly ultimately bounded stability of the affine nonlinear system is demonstrated with unknown time‐varying uncertainty. Finally, the performance of the proposed algorithm is demonstrated through a torsion pendulum system.

To address the issue of decreased positioning accuracy caused by interference or blockage of GNSS signals in vehicle navigation systems, this paper proposes a GNSS/MEMS IMU array fusion localization method based on an improved grey prediction model. First, a multi-feature fusion GNSS confidence evaluation algorithm is designed to assess the reliability of GNSS data in real time using indicators such as signal strength, satellite visibility, and solution consistency; second, to overcome the limitations of traditional grey prediction models in processing vehicle complex motion data, two key improvements are proposed: (1) a dynamic background value optimization method based on vehicle motion characteristics, which dynamically adjusts the weight coefficients in the background value construction according to vehicle speed, acceleration, and road curvature, enhancing the model’s sensitivity to changes in vehicle motion state; (2) a residual sequence compensation mechanism, which analyzes the variation patterns of historical residual sequences to accurately correct the prediction results, significantly improving the model’s prediction accuracy in nonlinear motion scenarios; finally, an adaptive fusion framework under normal and denied GNSS conditions is constructed, which directly fuses data when GNSS is reliable, and uses the improved grey model prediction results as virtual measurements for fusion during signal denial. Simulation and vehicle experiments verify that: compared to the traditional GM(1,1) model, the proposed method improves prediction accuracy by 31%, 52%, and 45% in straight, turning, and acceleration scenarios, respectively; in a 30-s GNSS denial scenario, the accuracy is improved by over 79% compared to pure INS methods.

Uncontrolled overactivation of autophagy may lead to autophagic cell death, suppression of which is a pro-survival strategy for tumors. However, mechanisms involving key regulators in modulating autophagic cell death remain poorly defined. Here, we report a novel long noncoding RNA, p53 upregulated regulator of p53 levels (PURPL), functions as an oncogene to promote cell proliferation, colony formation, migration, invasiveness, and inhibits cell death in melanoma cells. Mechanistic studies showed that PURPL promoted mTOR-mediated ULK1 phosphorylation at Ser757 by physical interacting with mTOR and ULK1 to constrain autophagic response to avoid cell death. Loss of PURPL led to AMPK-mediated phosphorylation of ULK1 at Ser555 and Ser317 to over-activate autophagy and induce autophagic cell death. Our results identify PURPL as a key regulator to modulate the activity of autophagy initiation factor ULK1 to repress autophagic cell death in melanoma and may represent a potential intervention target for melanoma therapy.

The latest climate models project widely varying magnitudes of future extreme precipitation changes, thus impeding effective adaptation planning. Many observational constraints have been proposed to reduce the uncertainty of these projections at global to sub-continental scales, but adaptation generally requires detailed, local scale information. Here, we present a temperature-based adaptative emergent constraint strategy combined with data aggregation that reduces the error variance of projected end-of-century changes in annual extremes of daily precipitation under a high emissions scenario by >20% across most areas of the world. These improved projections could benefit nearly 90% of the world’s population by permitting better impact assessment and adaptation planning at local levels. Our physically motivated strategy, which considers the thermodynamic and dynamic components of projected extreme precipitation change, exploits the link between global warming and the thermodynamic component of extreme precipitation. Rigorous cross-validation provides strong evidence of its reliability in constraining local extreme precipitation projections. This study presents more accurate future extreme precipitation projections at local scales constrained by past warming observations using an adaptative emergent constraint approach.

In this study, the hypocotyls of quinoa (cultivar ‘Qingqua I’) were used as the initial explants. The effects of plant growth regulators (PGRs) on induction of quinoa hypocotyl callus, adventitious shoots, and their elongation were investigated. The surface sterilization of seeds was conducted through sequential immersion in 75% (v/v) ethanol and 10% (v/v) sodium hypochlorite solution. The influence of proline concentrations for texture of callus, and macroelement levels of Murashige and Skoog (MS) medium for overcoming premature senescence of shoots were explored. The highest induction of callus (97.78 ± 1.92%) was observed in presence of 0.2 mg/L 6-benzyl adenine (BA) and 2 mg/L 1-naphthaleneacetic acid (NAA) in MS medium. The highest densities of callus (0.42 ± 0.006 g/cm 3 ) were achieved on MS medium containing from 1 mg/L BA, 0.1 mg/L NAA, and 0.2 g/L proline, which was optimum for induction of adventitious shoots, exhibiting the highest rate of adventitious shoot regeneration of 89.63 ± 2.67% and the highest adventitious shoot number of 40.50 ± 0.74. The best medium for overcoming the premature senescence of shoots was the medium with triple macronutrients (3MS) of 0.1 mg/L BA and 0.01 mg/L NAA. The exhibited a premature senescence rate of only 8.15 ± 1.09%, an elongation rate of 88.76 ± 1.14% of adventitious shoots, mean height of shoot per explant was 6.85 ± 0.02 cm, robust shoots and tender green leaves. For rooting, the best medium was the one which contained 0.6 mg/L indole-3-butyric acid (IBA), in which rooting was observed after 9.0 ± 0.3 d of rooting d, 98.52 ± 0.75% of rooting rate, and 16.90 ± 0.38 mean number of roots per explant were achieved. For the first time, we have overcome the challenge of premature senescence in quinoa, and this can be useful for other species.

Background Maresin-1 plays an important role in diabetic illnesses and ferroptosis is associated with pathogenic processes of diabetic retinopathy (DR). The goal of this study is to explore the influence of maresin-1 on ferroptosis and its molecular mechanism in DR. Methods ARPE-19 cells were exposed to high glucose (HG) condition for developing a cellular model of DR. The CCK-8 assay and flow cytometry were used to assess ARPE-19 cell proliferation and apoptosis, respectively. Furthermore, the GSH content, MDA content, ROS level, and Fe 2+ level were measured by using a colorimetric GSH test kit, a Lipid Peroxidation MDA Assay Kit, a DCFH-DA assay and the phirozine technique, respectively. Immunofluorescence labelling was used to detect protein levels of ACSL4 and PTGS2. Messenger RNA and protein expression of HO-1, GPX4 and Nrf2 was evaluated through western blotting and quantitative real time-polymerase chain reaction (qRT-PCR). To establish a diabetic mouse model, mice were intraperitoneally injected 150 mg/kg streptozotocin. The MDA content, ROS level and the iron level were detected by using corresponding commercial kits. Results Maresin-1 promoted cell proliferation while reducing the apoptotic process in HG-induced ARPE-19 cells. Maresin-1 significantly reduced ferroptosis induced by HG in ARPE-19 cells, as demonstrated as a result of decreased MDA content, ROS level, Fe 2+ level, PTGS2 expression, ACSL4 expression and increased GSH content. With respect to mechanisms, maresin-1 treatment up-regulated the mRNA expression and protein expression of HO-1, GPX4 and Nrf2 in HG-induced ARPE-19 cells. Nrf2 inhibitor reversed the inhibitory effects of maresin-1 on ferroptosis in HG-induced ARPE-19 cells. In vivo experiments, we found that Maresin-1 evidently repressed ferroptosis a mouse model of DR, as evidenced by the decreased MDA content, ROS level and iron level in retinal tissues of mice. Conclusion Maresin-1 protects ARPE cells from HG-induced ferroptosis via activating the Nrf2/HO-1/GPX4 pathway, suggesting that maresin-1 prevents DR development.

Visual geolocalization for aerial vehicles based on an analysis of Earth observation imagery is an effective method in GNSS-denied environments. However, existing methods for geographic location estimation have limitations: one relies on high-precision geodetic elevation data, which is costly, and the other assumes a flat ground surface, ignoring elevation differences. This paper presents a novel aerial vehicle geolocalization method. It integrates 2D information and relative depth information, which are both from Earth observation images. Firstly, the aerial and reference remote sensing satellite images are fed into a feature-matching network to extract pixel-level feature-matching pairs. Then, a depth estimation network is used to estimate the relative depth of the satellite remote sensing image, thereby obtaining the relative depth information of the ground area within the field of view of the aerial image. Finally, high-confidence matching pairs with similar depth and uniform distribution are selected to estimate the geographic location of the aerial vehicle. Experimental results demonstrate that the proposed method outperforms existing ones in terms of geolocalization accuracy and stability. It eliminates reliance on elevation data or planar assumptions, thus providing a more adaptable and robust solution for aerial vehicle geolocalization in GNSS-denied environments.